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<table width="100%" summary="page for S.alba"><tr><td>S.alba</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Potency of two herbicides</h2>

<h3>Description</h3>

<p>Data are from an experiment, comparing the potency of the two herbicides glyphosate and bentazone in
white mustard <em>Sinapis alba</em>.
</p>


<h3>Usage</h3>

<pre>data(S.alba)</pre>


<h3>Format</h3>

<p>A data frame with 68 observations on the following 3 variables.
</p>

<dl>
<dt><code>Dose</code></dt><dd><p>a numeric vector containing the dose in g/ha.</p>
</dd>
<dt><code>Herbicide</code></dt><dd><p>a factor with levels <code>Bentazone</code> <code>Glyphosate</code> (the two herbicides applied).</p>
</dd>
<dt><code>DryMatter</code></dt><dd><p>a numeric vector containing the response (dry matter in g/pot).</p>
</dd>
</dl>



<h3>Details</h3>

<p>The lower and upper limits for the two herbicides can be assumed identical, whereas slopes and ED50 values 
are different (in the log-logistic model).
</p>


<h3>Source</h3>

<p>Christensen, M. G. and Teicher, H. B., and Streibig, J. C. (2003) Linking fluorescence 
induction curve and biomass in herbicide screening, <em>Pest Management Science</em>,
<b>59</b>,  1303&ndash;1310.
</p>


<h3>See Also</h3>

<p>See the examples sections for <code>drm</code> and <code>EDcomp</code>.
</p>


<h3>Examples</h3>

<pre>

## Fitting a log-logistic model with
##  common lower and upper limits
S.alba.LL.4.1 &lt;- drm(DryMatter~Dose, Herbicide, data=S.alba, fct = LL.4(),
pmodels=data.frame(Herbicide,1,1,Herbicide)) 
summary(S.alba.LL.4.1)

## Applying the optimal transform-both-sides Box-Cox transformation
## (using the initial model fit)  
S.alba.LL.4.2 &lt;- boxcox(S.alba.LL.4.1, method = "anova") 
summary(S.alba.LL.4.2)

## Plotting fitted regression curves together with the data
plot(S.alba.LL.4.2)

</pre>


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